(Published in Mid-Range ERP magazine November 1997)
this article was published almost a decade ago, the tenets and concepts expressed
herein continue to hold true today. In fact, these beliefs have become all the more
important as we participate in an increasingly globalized business environment.
Forecasting is a subject increasingly under scrutiny as corporations
look to improve their business processes and profitability. As the business
world becomes ever more competitive, many organizations are finding that
better forecasting benefits their bottom line directly. This article is
an attempt to clarify some concepts of forecasting, and to discuss some
of the general issues that must be dealt with when designing a forecasting
system and process.
A good place to start is to define the term forecasting, which
is often misunderstood. Forecasting is a process of using various
tools and techniques to anticipate the amounts and/or values of
future sales of products and services. It is not a measurement
of a known quantity, but an attempt to combine historically observed
patterns with "known" influencing events to come up with a "most
educated guess" as to what the future holds. Forecasting is therefore
a planning activity, fundamentally different from most measurement
activities. It is constantly focused not on what has happened
but on what might happen. Whereas someone might set out to become
proficient at accounting, and become virtually perfect at keeping
debits and credits balanced, in forecasting there is always a
degree of uncertainty and error that no amount of skill or training
will overcome. The challenge is to set up software systems as
well as processes of information flow that allow getting closer
to the desired benchmark of accuracy.
Given the difficulties inherent in forecasting, companies have in the
past been tempted to neglect this area of activity. They are reminded,
however, by some of the best-known consultants, that this cannot be overlooked.
According to "The Oliver Wight ABCD Checklist for Operational Excellence",
in order for a manufacturing organization to qualify as a "Class A" MRP
"There is a process for forecasting all anticipated demands with sufficient
detail and adequate planning horizon to support business planning, sales
and operations planning, and master production scheduling. Forecast accuracy
is measured in order to continuously improve the process."
In other words, not only is this activity essential, but it's accuracy
is a metric that should be monitored. Although each year brings new gurus
and acronyms to the industry, this is one of the basic concepts that has
Unfortunately, while the importance of forecasting has been recognized,
there has been a shortage of information and training as to how an effective
forecasting process should be designed. As already mentioned, it involves
not only software systems, but also processes where people exchange information.
As such it is not an exact science, but to a certain extent an art as
well. We have to accept a basic premise: The forecasts are always wrong!
(If you were 100% accurate, you would never be wrong). The focus becomes
"Being less wrong, as consistently as possible". The focus is also on
on-going review and corrective action to do better next time.
The next issue is to discuss who, meaning what kind of organizations,
need to forecast. Traditionally, forecasting was used in make-to-stock
environments, where companies would maintain inventories of regularly
sold products, since they were never quite sure what the demand would
be, and would therefore maintain cycle stock plus some safety stock. This
could apply to products manufactured or distributed. In a make-to-order
environment, companies traditionally thought it was unnecessary to forecast.
When an order came in from a customer, that order was executed because
it in effect was the forecast. This is changing rapidly however, as customers
have become far more demanding, and expect very short turn-around times
between order and delivery, thus forcing make-to-order companies to start
anticipating demands before they become known.
Once an organization has decided to start forecasting, a crucial decision
is the level of detail to choose. Data is usually available from transaction
detail right up to corporate totals. Transactions are usually not forecastable,
it is best to work with summarized snapshots, such as weekly, 13-period
or monthly data. Data can also be looked at at various levels of corporate
reporting hierarchies (Company vs. Division vs. Product family vs. SKU)
, or segmented geographically (Country vs. Region vs. Sales Territory
vs. Customer). The impacts of this decision are very far-reaching. If
you forecast at a high level of summarization, you will likely achieve
very accurate results, but the forecast will be not very useful to running
the enterprise. If you forecast at too low a level of detail, you will
be overburdening your users, and the accuracy will be poor. The rule of
thumb is: As the level of detail increases, the workload increases dramatically,
while accuracy may increase to a point and then will drop off. In other
words, it is crucial to find an optimal level of detail / summarization
to forecast at, where your forecasters have a reasonable workload, and
where they can still add value to the forecasts. Bear in mind that for
best results, the forecast is created by a combination of machine-generated
baseline forecasts combined with user intervention. A key ratio that should
be looked at is: Forecasted Items/Forecasters.
Another important driver of the process design will be to determine
who the internal customers of the forecasts will be. Is the forecast
primarily to feed MRP / Master Production Scheduling or is it
to help Marketing with their planning? Is it Sales-driven and
also used to set Sales quotas and budgets, or is the priority
to allow Finance a pro-forma look at revenues derived from product
sales? Does Senior Management look at the forecast as a "wished-for"
number or will it be used to drive Distribution Center replenishment
? Is the corporate goal a true "1-number forecast" as per the
MRP II concept? Will your external customers need to access the
forecasts via Internet, or are the forecasts strictly for in-house
consumption? The answers to these types of questions go a long
way to determining how to set up a process that will serve the
needs of all the recipients of the forecast numbers.
The next major decision involves the tools used to generate the forecasts.
Organizations using mainframe or legacy forecasting tools are under pressure
from users to move to forecasting applications using PC and Windows technology,
not only to achieve better integration with other applications, but also
to improve ease-of-use with GUI interfaces, ensure Year 2000 compliance
and Internet capability. Sales-driven environments are faced with the
challenge of improving forecasting in the context of Sales Force Automation.
Defining who should have the responsibility of forecasting can also
be a difficult and politically charged decision. If the decision to forecast
is driven by a need to feed production scheduling or warehouse replenishment,
this will often mean ownership of the process in logistics. An organizations
defined along business unit lines or using a customer service team approach
will often have forecasting done by Marketing managers or Customer Service
managers as one of many functions. Other more centrally organized companies
may employ full-time demand planners or forecasting teams that do nothing
The methodology of forecasting used to be exclusively in the domain of
statistical techniques, which would utilize various mathematical models
to capture trend and seasonality patterns from historical data, and extrapolate
these into the future. Measures of accuracy were also traditional mathematical
metrics such as R-squared, or Mean Absolute Deviation (MAD). This type
of machine-driven baseline forecast continues to provide many benefits,
due to the automation and time-savings it entails. It worked well in the
past for very stable businesses, but is missing a required dimension for
the majority of todayıs enterprises.
The additional component needed is that of marketing events or promotions
determined from outside the company. These events could range from competitive
product launches, government regulatory intervention, pricing and promotional
events determined by customers, and more. Any knowledge of such events
is usually partial and at best an educated guess. However, most Sales
or Marketing people usually have some sense of such upcoming events. This
understanding may be as vague as a "gut-feel", or be as concrete as a
promise by a Customer to deliver a certain volume of sales on a promotion.
The expectation may change frequently as the event gets closer, the timing
and magnitude may vary as well, reinforcing our statement that this is
more an art than a science. However, this judgmental knowledge of influencing
events is crucial data that needs to be incorporated into the forecasts,
and can add tremendous value to it. These expected events become documented
assumptions underlying the forecast. The requirement of a forecasting
system is therefore to allow easy mechanisms for integrating such knowledge,
and overlaying it onto a mechanically produced baseline forecast. Measurements
of accuracy also have to change along with this methodology, and focus
more on variance of actual from forecast than traditional statistical
Another important aspect to the design a forecasting system and process
relates to the sources of data. In the case of creating the baseline machine-generated
forecast, the source of data is usually the host system or enterprise
system, whether mainframe, mid-range or network-based. Historical data
is usually collected from Order Entry/Sales tables, from where it can
be downloaded to PC level. Ideally such an interface uses non-proprietary
technology such as ODBC. Finished forecasts are most often uploaded into
Master Production Scheduling, or in some cases into other planning and
simulation systems, reporting systems or even Data Warehouses. The source
of Market Event data is usually more difficult, as many companies do not
formally collect such information, and would rely on a forecasting system
to provide such a mechanism. If it is being collected, it is likely in
the form of spreadsheets or other PC-based applications and has to be
integrated from there.
In summary, organizations that have reached the goal of optimizing
a forecasting process, have done so by combining the ingredients
of up-to-date forecast software systems, a people-driven process
of capturing and integrating market events, and measuring / tracking
forecast accuracy to obtain continuous improvement.